, Volume 69, Issue 1–2, pp 81–91 | Cite as

Metrics for the comparative analysis of geospatial datasets with applications to high-resolution grid-based population data

  • Aarthy Sabesan
  • Kathleen Abercrombie
  • Auroop R. Ganguly
  • Budhendra Bhaduri
  • Eddie A. Bright
  • Phillip R. Coleman


Geospatial data sciences have emerged as critical requirements for high-priority application solutions in diverse areas, including, but not limited to, the mitigation of natural and man-made disasters. Three sets of metrics, adopted or customized from geo-statistics, applied meteorology and signal processing, are tested in terms of their ability to evaluate geospatial datasets, specifically two population databases commonly used for disaster preparedness and consequence management. The two high-resolution, grid-based population datasets are the following: The LandScan dataset available from the Geographic Information Science and Technology (GIST) group at the Oak Ridge National Laboratory (ORNL), and the Gridded Population of the World (GPW) dataset available from the Center for International Earth Science Information Network (CIESIN) group at Columbia University. Case studies evaluate population data across the globe, specifically, the metropolitan areas of Washington DC, USA, Los-Angeles, USA, and Houston, USA, and London, UK, as well as the country of Iran. The geospatial metrics confirm that the two population datasets have significant differences, especially in the context of their utility for disaster readiness and mitigation. While this paper primarily focuses on grid based population datasets and disaster management applications, the sets of metrics developed here can be generalized to other geospatial datasets and applications. Future research needs to develop metrics for geospatial and temporal risks and associated uncertainties in the context of disaster management.


Geospatial data Population Statistical evaluation Disaster management 



Funding support for developing the LandScan Global and LandScan USA databases is provided by several United States government agencies. The authors would like to thank Amy L. King and internal technical and editorial reviewers at Oak Ridge National Laboratory for their assistance. The proceedings have been co-authored by UT Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains, and the publisher by accepting the article for publication, acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.


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Copyright information

© Springer Science + Business Media B.V. 2007

Authors and Affiliations

  • Aarthy Sabesan
    • 1
  • Kathleen Abercrombie
    • 2
  • Auroop R. Ganguly
    • 2
  • Budhendra Bhaduri
    • 2
  • Eddie A. Bright
    • 2
  • Phillip R. Coleman
    • 2
  1. 1.Computational Sciences and EngineeringOak Ridge National LaboratoryOak RidgeUSA
  2. 2.Computational Sciences and EngineeringOak Ridge National LaboratoryOak RidgeUSA

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